{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.models as models\n",
    "from utils import Dataset\n",
    "import torch.nn.functional as F\n",
    "from torch import optim\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "rcParams['figure.figsize'] = (12,6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup a baseline to compare k-winners, which is fast to train and evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = Dataset(config=dict(dataset_name='MNIST', data_dir='~/nta/datasets',\n",
    "                             batch_size_train=256, batch_size_test=1024))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# torch cross_entropy is log softmax activation + negative log likelihood\n",
    "loss_func = F.cross_entropy\n",
    "\n",
    "# a custom Lambda module\n",
    "class Lambda(nn.Module):\n",
    "    def __init__(self, func):\n",
    "        super().__init__()\n",
    "        self.func = func\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.func(x)\n",
    "\n",
    "# simple feedforward model\n",
    "# use a lambda layer to resize \n",
    "model = nn.Sequential(\n",
    "    Lambda(lambda x: x.view(-1,28*28)),\n",
    "    nn.Linear(784,100),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(100,10),\n",
    ")\n",
    "  \n",
    "# calculate accuracy\n",
    "def accuracy(loader, num_batches=3):\n",
    "    len_dataset = loader.dataset.data.size()[0]\n",
    "    running_acc = 0\n",
    "    running_count = 0\n",
    "    # do no cover entire dataset. training is shuffled, testing is not\n",
    "    iter_loader = iter(loader)\n",
    "    for _ in range(num_batches):\n",
    "        x,y = next(iter_loader)\n",
    "        out = model(x)\n",
    "        preds = torch.argmax(out, dim=1)\n",
    "        running_acc += (preds == y).float().sum()\n",
    "        running_count += x.size()[0]\n",
    "        \n",
    "    return running_acc.item() / running_count  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "# baseline\n",
    "def fit(model, dataset, verbose=True, epochs=1, epoch_eval=True):\n",
    "    test_accuracies = []\n",
    "    losses = []\n",
    "    # dataset\n",
    "    train_loader = dataset.train_loader\n",
    "    test_loader = dataset.test_loader\n",
    "    # hyperparams\n",
    "    opt = optim.SGD(model.parameters(), lr=.01, momentum=0.9)\n",
    "    num_batches = 60\n",
    "    # training loop\n",
    "    print(\"Training Accuracy before training: {:.4f}\".format(accuracy(train_loader)))\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        iter_loader = iter(train_loader)    \n",
    "        for i in range(num_batches):\n",
    "            x,y = next(iter_loader)\n",
    "            # calculate loss\n",
    "            loss = loss_func(model(x), y)\n",
    "            losses.append(loss.item())\n",
    "            # backpropagate\n",
    "            loss.backward()\n",
    "            # learn\n",
    "            opt.step()\n",
    "            opt.zero_grad()\n",
    "            if verbose:\n",
    "                if i % 20 == 0:\n",
    "                    print(\"Loss: {:.8f}\".format(loss.item()*1000/len(x)))\n",
    "        \n",
    "        if epoch_eval:\n",
    "            model.eval()\n",
    "            test_acc = accuracy(test_loader)\n",
    "            test_accuracies.append(test_acc)\n",
    "        \n",
    "    model.eval()\n",
    "    print(\"Training Accuracy after training: {:.4f}\".format(accuracy(train_loader)))\n",
    "    print(\"Test Accuracy after training: {:.4f}\".format(accuracy(test_loader)))\n",
    "    print(\"---------------------------\")\n",
    "    \n",
    "    return losses, test_accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.1680\n",
      "Loss: 8.86095315\n",
      "Loss: 3.45818489\n",
      "Loss: 1.58749637\n",
      "Training Accuracy after training: 0.8906\n",
      "Test Accuracy after training: 0.8617\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "fit(model, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extend for the k-Winners simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.0964\n",
      "Loss: 9.14294273\n",
      "Loss: 4.20531072\n",
      "Loss: 1.95618416\n",
      "Training Accuracy after training: 0.8529\n",
      "Test Accuracy after training: 0.8109\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "# from functions import KWinnersBatch as KWinners\n",
    "from functions import KWinners\n",
    "model_gen = lambda k: nn.Sequential(\n",
    "    Lambda(lambda x: x.view(-1,28*28)),\n",
    "    nn.Linear(784,100),\n",
    "    KWinners(k_perc=k),\n",
    "    nn.Linear(100,10),\n",
    ")\n",
    "\n",
    "model = model_gen(.1)\n",
    "fit(model, dataset, epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K: 0.010000\n",
      "Training Accuracy before training: 0.0977\n",
      "Training Accuracy after training: 0.1823\n",
      "Test Accuracy after training: 0.2087\n",
      "---------------------------\n",
      "K: 0.110000\n",
      "Training Accuracy before training: 0.1133\n",
      "Training Accuracy after training: 0.7305\n",
      "Test Accuracy after training: 0.7109\n",
      "---------------------------\n",
      "K: 0.210000\n",
      "Training Accuracy before training: 0.0898\n",
      "Training Accuracy after training: 0.8203\n",
      "Test Accuracy after training: 0.8037\n",
      "---------------------------\n",
      "K: 0.310000\n",
      "Training Accuracy before training: 0.0911\n",
      "Training Accuracy after training: 0.8529\n",
      "Test Accuracy after training: 0.8411\n",
      "---------------------------\n",
      "K: 0.410000\n",
      "Training Accuracy before training: 0.1068\n",
      "Training Accuracy after training: 0.8477\n",
      "Test Accuracy after training: 0.8245\n",
      "---------------------------\n",
      "K: 0.510000\n",
      "Training Accuracy before training: 0.0755\n",
      "Training Accuracy after training: 0.8815\n",
      "Test Accuracy after training: 0.8486\n",
      "---------------------------\n",
      "K: 0.610000\n",
      "Training Accuracy before training: 0.1172\n",
      "Training Accuracy after training: 0.8490\n",
      "Test Accuracy after training: 0.8493\n",
      "---------------------------\n",
      "K: 0.710000\n",
      "Training Accuracy before training: 0.1406\n",
      "Training Accuracy after training: 0.9023\n",
      "Test Accuracy after training: 0.8597\n",
      "---------------------------\n",
      "K: 0.810000\n",
      "Training Accuracy before training: 0.0352\n",
      "Training Accuracy after training: 0.8828\n",
      "Test Accuracy after training: 0.8545\n",
      "---------------------------\n",
      "K: 0.910000\n",
      "Training Accuracy before training: 0.1445\n",
      "Training Accuracy after training: 0.8750\n",
      "Test Accuracy after training: 0.8525\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in np.arange(0.01,1,0.1):\n",
    "    print(\"K: %f\" % k)\n",
    "    model = model_gen(k) \n",
    "    fit(model, dataset, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model accuracy with 1% of active neurons, with and without boosting:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Without boosting:\n",
    "Training Accuracy before training: 0.0768\n",
    "Training Accuracy after training: 0.3529\n",
    "Test Accuracy after training: 0.3558\n",
    "\n",
    "With boosting:\n",
    "Training Accuracy before training: 0.0911\n",
    "Training Accuracy after training: 0.8685\n",
    "Test Accuracy after training: 0.8444"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.0768\n",
      "Loss: 9.16984864\n",
      "Loss: 2.52443319\n",
      "Loss: 1.56559551\n",
      "Training Accuracy after training: 0.8919\n",
      "Test Accuracy after training: 0.8630\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "# no non-linearity required to get a low accuracy\n",
    "model = nn.Sequential(\n",
    "    Lambda(lambda x: x.view(-1,28*28)),\n",
    "    nn.Linear(784,100),\n",
    "    nn.Linear(100,10),\n",
    ")\n",
    "fit(model, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extend to CNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further tests and comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.0977\n",
      "Loss: 9.01591964\n",
      "Loss: 8.31630360\n",
      "Loss: 2.41597765\n",
      "Loss: 1.87853654\n",
      "Loss: 2.01450428\n",
      "Loss: 1.43476040\n",
      "Loss: 1.32107059\n",
      "Loss: 1.22576545\n",
      "Loss: 0.77787635\n",
      "Loss: 0.91777480\n",
      "Loss: 0.80468395\n",
      "Loss: 0.97725971\n",
      "Loss: 0.91118726\n",
      "Loss: 0.53855963\n",
      "Loss: 0.57668181\n",
      "Loss: 0.66801795\n",
      "Loss: 0.61882939\n",
      "Loss: 0.38357661\n",
      "Loss: 0.64914837\n",
      "Loss: 0.52336586\n",
      "Loss: 0.62132406\n",
      "Loss: 0.46719360\n",
      "Loss: 0.47875027\n",
      "Loss: 0.46436820\n",
      "Loss: 0.64608682\n",
      "Loss: 0.40764842\n",
      "Loss: 0.27636855\n",
      "Loss: 0.37904861\n",
      "Loss: 0.28149094\n",
      "Loss: 0.42086712\n",
      "Loss: 0.32278395\n",
      "Loss: 0.28765490\n",
      "Loss: 0.59745915\n",
      "Loss: 0.46983990\n",
      "Loss: 0.62205613\n",
      "Loss: 0.29754065\n",
      "Loss: 0.22269128\n",
      "Loss: 0.43646299\n",
      "Loss: 0.23136851\n",
      "Loss: 0.18707171\n",
      "Loss: 0.21003128\n",
      "Loss: 0.39961765\n",
      "Loss: 0.18117669\n",
      "Loss: 0.16319507\n",
      "Loss: 0.32178446\n",
      "Loss: 0.15433319\n",
      "Loss: 0.24375597\n",
      "Loss: 0.22575687\n",
      "Loss: 0.14690486\n",
      "Loss: 0.30929525\n",
      "Loss: 0.09941603\n",
      "Loss: 0.30509694\n",
      "Loss: 0.13194060\n",
      "Loss: 0.24089994\n",
      "Loss: 0.25497915\n",
      "Loss: 0.23787981\n",
      "Loss: 0.34323748\n",
      "Loss: 0.10587521\n",
      "Loss: 0.45740310\n",
      "Loss: 0.25988219\n",
      "Training Accuracy after training: 0.9857\n",
      "Test Accuracy after training: 0.9727\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "# simple CNN Model\n",
    "non_linearity = nn.ReLU\n",
    "model = nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    non_linearity(),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    non_linearity(),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    non_linearity(),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "losses, accs = fit(model, dataset, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.1029\n",
      "Loss: 8.96801241\n",
      "Loss: 3.06582148\n",
      "Loss: 1.94012583\n",
      "Loss: 2.02102750\n",
      "Loss: 1.34406553\n",
      "Loss: 1.87980209\n",
      "Loss: 1.13920833\n",
      "Loss: 1.22467999\n",
      "Loss: 1.49299891\n",
      "Loss: 1.22825115\n",
      "Loss: 1.60129461\n",
      "Loss: 1.23011169\n",
      "Loss: 1.36455195\n",
      "Loss: 1.39456335\n",
      "Loss: 1.48341618\n",
      "Loss: 1.41690241\n",
      "Loss: 1.20578543\n",
      "Loss: 1.52827904\n",
      "Loss: 1.05158892\n",
      "Loss: 1.27736642\n",
      "Loss: 1.81873434\n",
      "Loss: 1.23656099\n",
      "Loss: 1.15134846\n",
      "Loss: 1.55259133\n",
      "Loss: 1.21808599\n",
      "Loss: 1.27092609\n",
      "Loss: 1.08659372\n",
      "Loss: 1.27634523\n",
      "Loss: 1.21774606\n",
      "Loss: 1.42457709\n",
      "Loss: 1.00178621\n",
      "Loss: 1.31482806\n",
      "Loss: 1.15789764\n",
      "Loss: 1.32989080\n",
      "Loss: 1.24805828\n",
      "Loss: 1.18830928\n",
      "Loss: 0.91247406\n",
      "Loss: 1.32787740\n",
      "Loss: 1.55617401\n",
      "Loss: 1.15874608\n",
      "Loss: 0.96375117\n",
      "Loss: 1.62406103\n",
      "Loss: 1.44307211\n",
      "Loss: 1.08400709\n",
      "Loss: 0.92198735\n",
      "Loss: 1.26788346\n",
      "Loss: 1.42064027\n",
      "Loss: 1.18913536\n",
      "Loss: 1.29507936\n",
      "Loss: 1.01640844\n",
      "Loss: 1.65735430\n",
      "Loss: 1.28937559\n",
      "Loss: 1.04315509\n",
      "Loss: 0.90763328\n",
      "Loss: 0.94556110\n",
      "Loss: 0.92524861\n",
      "Loss: 1.32520474\n",
      "Loss: 1.52133079\n",
      "Loss: 1.36286218\n",
      "Loss: 0.68302959\n",
      "Training Accuracy after training: 0.9206\n",
      "Test Accuracy after training: 0.8870\n",
      "---------------------------\n"
     ]
    }
   ],
   "source": [
    "# kWinners\n",
    "from functions import KWinners\n",
    "model_gen = lambda k: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "model = model_gen(k=0.25)\n",
    "kw_losses, kw_accs = fit(model, dataset, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.0781\n",
      "Loss: 8.98863841\n",
      "Loss: 8.51961784\n",
      "Loss: 3.89555935\n",
      "Loss: 2.16993084\n",
      "Loss: 1.74177578\n",
      "Loss: 1.29505887\n",
      "Loss: 1.54911226\n",
      "Loss: 1.37490628\n",
      "Loss: 1.58173812\n",
      "Loss: 0.89562114\n",
      "Loss: 1.21299771\n",
      "Loss: 1.08736753\n",
      "Loss: 1.01180549\n",
      "Loss: 0.94258174\n",
      "Loss: 0.67333027\n",
      "Loss: 1.15846109\n",
      "Loss: 0.58816507\n",
      "Loss: 0.48005674\n",
      "Loss: 0.37854345\n",
      "Loss: 0.56073791\n",
      "Loss: 0.62870054\n",
      "Loss: 0.66439097\n",
      "Loss: 0.42711181\n",
      "Loss: 0.60873589\n",
      "Loss: 0.36493287\n",
      "Loss: 0.44638448\n",
      "Loss: 0.28564816\n",
      "Loss: 0.40550501\n",
      "Loss: 0.46862161\n",
      "Loss: 0.54111570\n",
      "Loss: 0.39297619\n",
      "Loss: 0.66374434\n",
      "Loss: 0.40825043\n",
      "Loss: 0.35721733\n",
      "Loss: 0.25609633\n",
      "Loss: 0.27336623\n",
      "Loss: 0.43698720\n",
      "Loss: 0.45553839\n",
      "Loss: 0.30519644\n",
      "Loss: 0.36657095\n",
      "Loss: 0.43717056\n",
      "Loss: 0.27034053\n",
      "Loss: 0.30268749\n",
      "Loss: 0.25070496\n",
      "Loss: 0.22197841\n",
      "Loss: 0.43891702\n",
      "Loss: 0.38769090\n",
      "Loss: 0.39445105\n",
      "Loss: 0.20566507\n",
      "Loss: 0.50744601\n",
      "Loss: 0.32244410\n",
      "Loss: 0.38550625\n",
      "Loss: 0.37734438\n",
      "Loss: 0.74301276\n",
      "Loss: 0.37948275\n",
      "Loss: 0.28699744\n",
      "Loss: 0.31794142\n",
      "Loss: 0.34758437\n",
      "Loss: 0.36032515\n",
      "Loss: 0.29542844\n",
      "Training Accuracy after training: 0.9766\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.9626\n"
     ]
    }
   ],
   "source": [
    "# kWinners\n",
    "from functions import KWinners\n",
    "model_gen = lambda k: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k, use_absolute=False),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k, use_absolute=False),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k, use_absolute=False),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "model = model_gen(k=0.25)\n",
    "kwp_losses, kwp_accs = fit(model, dataset, epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(kw_losses, label='kw_abs_losses')\n",
    "plt.plot(kwp_losses, label='kw_pos_losses')\n",
    "plt.plot(losses, label='losses')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate the role of boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.1263\n",
      "Loss: 8.99374299\n",
      "Loss: 8.70009977\n",
      "Loss: 4.99155512\n",
      "Training Accuracy after training: 0.8307\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.8138\n"
     ]
    }
   ],
   "source": [
    "# kWinners without boosting\n",
    "from functions import KWinners\n",
    "model_gen = lambda k: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k, use_boosting=False),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k, use_boosting=False),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k, use_boosting=False),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "model = model_gen(k=0.1)\n",
    "fit(model, dataset, epochs=1, epoch_eval=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy before training: 0.1224\n",
      "Loss: 9.01356246\n",
      "Loss: 8.69631581\n",
      "Loss: 5.94143802\n",
      "Training Accuracy after training: 0.7982\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7930\n"
     ]
    }
   ],
   "source": [
    "# kWinners with boosting\n",
    "from functions import KWinners\n",
    "model_gen = lambda k: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k, use_boosting=True),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k, use_boosting=True),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k, use_boosting=True),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "model = model_gen(k=0.1)\n",
    "fit(model, dataset, epochs=1, epoch_eval=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta: 0.000000\n",
      "Training Accuracy before training: 0.0898\n",
      "Loss: 8.98815040\n",
      "Loss: 8.77536274\n",
      "Loss: 6.98788743\n",
      "Training Accuracy after training: 0.7917\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7992\n",
      "Beta: 0.010000\n",
      "Training Accuracy before training: 0.1198\n",
      "Loss: 8.99235159\n",
      "Loss: 8.80934391\n",
      "Loss: 7.56296981\n",
      "Training Accuracy after training: 0.7617\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7676\n",
      "Beta: 0.020000\n",
      "Training Accuracy before training: 0.1406\n",
      "Loss: 9.01190378\n",
      "Loss: 8.72939453\n",
      "Loss: 6.65350724\n",
      "Training Accuracy after training: 0.7734\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7760\n",
      "Beta: 0.050000\n",
      "Training Accuracy before training: 0.1133\n",
      "Loss: 8.99773650\n",
      "Loss: 8.86656344\n",
      "Loss: 8.14315118\n",
      "Training Accuracy after training: 0.7604\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7503\n",
      "Beta: 0.100000\n",
      "Training Accuracy before training: 0.0781\n",
      "Loss: 9.00390372\n",
      "Loss: 8.86570197\n",
      "Loss: 8.14782269\n",
      "Training Accuracy after training: 0.7669\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.7568\n"
     ]
    }
   ],
   "source": [
    "# Exploring several values for beta\n",
    "from functions import KWinners\n",
    "model_gen = lambda k,b: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "for b in [0, 0.01, 0.02, 0.05, 0.1]:\n",
    "    model = model_gen(k=0.1, b=b)\n",
    "    print(\"Beta: %f\" % b)\n",
    "    fit(model, dataset, epochs=1, epoch_eval=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta: 0.000000\n",
      "Training Accuracy before training: 0.0872\n",
      "Training Accuracy after training: 0.9922\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.9746\n",
      "Beta: 0.002000\n",
      "Training Accuracy before training: 0.0612\n",
      "Training Accuracy after training: 0.9896\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.9736\n",
      "Beta: 0.010000\n",
      "Training Accuracy before training: 0.1003\n",
      "Training Accuracy after training: 0.9935\n",
      "---------------------------\n",
      "Test Accuracy after training: 0.9723\n"
     ]
    }
   ],
   "source": [
    "# Exploring several values for beta\n",
    "from functions import KWinners\n",
    "model_gen = lambda k,b: nn.Sequential(\n",
    "    nn.Conv2d(1,32, kernel_size=3, stride=2, padding=1), # 14x14\n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    nn.Conv2d(32,64, kernel_size=3, stride=2, padding=1), # 7x7\n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    nn.Conv2d(64,128, kernel_size=3, stride=2, padding=1), # 4x4 \n",
    "    KWinners(k_perc=k, use_boosting=True, beta=b),\n",
    "    Lambda(lambda x: x.view(x.size(0), -1)), # 128    \n",
    "    nn.Linear(128*4*4,10) # 10\n",
    ")\n",
    "\n",
    "betas, losses, accs = [], [] ,[]\n",
    "for b in [0, 0.002, 0.01]:\n",
    "    model = model_gen(k=0.1, b=b)\n",
    "    print(\"Beta: %f\" % b)\n",
    "    loss, acc = fit(model, dataset, epochs=50, epoch_eval=True, verbose=False);\n",
    "    betas.append(b)\n",
    "    losses.append(loss)\n",
    "    accs.append(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot loss and acc for different betas and answer the question if boosting helps or hurts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 930
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (16,8)\n",
    "for beta, loss in zip(betas, losses):\n",
    "    plt.plot(loss, label=str(beta))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 936
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for beta, acc in zip(betas, accs):\n",
    "    plt.plot(acc, label=str(beta))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploration on how torch.expand is working in the background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topk experimentation\n",
    "t = torch.randn((4,3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.8587,  0.3650],\n",
       "         [ 1.0872,  0.1732],\n",
       "         [-0.1931, -1.3085]],\n",
       "\n",
       "        [[-0.0944,  1.3081],\n",
       "         [ 0.0840,  0.4794],\n",
       "         [-0.1854, -1.8701]],\n",
       "\n",
       "        [[-0.5506, -1.2746],\n",
       "         [ 0.9189,  1.3216],\n",
       "         [ 0.0577,  0.7018]],\n",
       "\n",
       "        [[-0.9030, -1.7370],\n",
       "         [-0.9755,  0.0730],\n",
       "         [-0.4975,  1.5974]]])"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = torch.ones((3,2)) * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2., 2.],\n",
       "        [2., 2.],\n",
       "        [2., 2.]])"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 3, 2]), torch.Size([3, 2]))"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape, b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[2., 2.],\n",
       "         [2., 2.],\n",
       "         [2., 2.]],\n",
       "\n",
       "        [[2., 2.],\n",
       "         [2., 2.],\n",
       "         [2., 2.]],\n",
       "\n",
       "        [[2., 2.],\n",
       "         [2., 2.],\n",
       "         [2., 2.]],\n",
       "\n",
       "        [[2., 2.],\n",
       "         [2., 2.],\n",
       "         [2., 2.]]])"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.expand((4,3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.7174,  0.7301],\n",
       "         [ 2.1744,  0.3463],\n",
       "         [-0.3862, -2.6169]],\n",
       "\n",
       "        [[-0.1888,  2.6162],\n",
       "         [ 0.1679,  0.9589],\n",
       "         [-0.3709, -3.7401]],\n",
       "\n",
       "        [[-1.1013, -2.5493],\n",
       "         [ 1.8378,  2.6431],\n",
       "         [ 0.1153,  1.4037]],\n",
       "\n",
       "        [[-1.8060, -3.4740],\n",
       "         [-1.9510,  0.1460],\n",
       "         [-0.9951,  3.1948]]])"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t * b.expand((4,3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploration on how to select kth winners in torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topk experimentation\n",
    "t = torch.randn((4,3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.8587,  0.3650],\n",
       "         [ 1.0872,  0.1732],\n",
       "         [-0.1931, -1.3085]],\n",
       "\n",
       "        [[-0.0944,  1.3081],\n",
       "         [ 0.0840,  0.4794],\n",
       "         [-0.1854, -1.8701]],\n",
       "\n",
       "        [[-0.5506, -1.2746],\n",
       "         [ 0.9189,  1.3216],\n",
       "         [ 0.0577,  0.7018]],\n",
       "\n",
       "        [[-0.9030, -1.7370],\n",
       "         [-0.9755,  0.0730],\n",
       "         [-0.4975,  1.5974]]])"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 6])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([-1.3085, -1.8701, -1.2746, -1.7370])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tx = t.view(t.size()[0], -1)\n",
    "print(tx.size())\n",
    "val, _ = torch.kthvalue(tx, 1, dim=-1)\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 1, 1]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[t.size()[0]] + [1 for _ in range(len(t.size())-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 2])"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 2])"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(t > val.view(4,1,1)).sum(dim=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4, 2],\n",
       "        [4, 4],\n",
       "        [4, 2]])"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(t > val.view(4,1,1)).sum(dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1, 1],\n",
       "         [1, 1],\n",
       "         [1, 0]],\n",
       "\n",
       "        [[1, 1],\n",
       "         [1, 1],\n",
       "         [1, 0]],\n",
       "\n",
       "        [[1, 0],\n",
       "         [1, 1],\n",
       "         [1, 1]],\n",
       "\n",
       "        [[1, 0],\n",
       "         [1, 1],\n",
       "         [1, 1]]], dtype=torch.uint8)"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t > val.view(4,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 2])"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 1, 1])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.view(4,1,1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-1.3085]],\n",
       "\n",
       "        [[-1.8701]],\n",
       "\n",
       "        [[-1.2746]],\n",
       "\n",
       "        [[-1.7370]]])"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val.view(4,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "val, ind = torch.topk(t, k=1, dim=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1],\n",
       "         [0],\n",
       "         [0]],\n",
       "\n",
       "        [[1],\n",
       "         [1],\n",
       "         [0]],\n",
       "\n",
       "        [[0],\n",
       "         [1],\n",
       "         [1]],\n",
       "\n",
       "        [[0],\n",
       "         [1],\n",
       "         [1]]])"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 2])"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 10])"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 2, 10])"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[ind].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 10])"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0., 1., 0., 0., 0., 1., 0., 0.],\n",
       "        [0., 0., 0., 1., 0., 0., 0., 1., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 0., 0., 0., 1., 0.],\n",
       "        [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 0., 1., 0., 1., 0., 0.],\n",
       "        [0., 0., 0., 1., 0., 1., 0., 0., 0., 0.],\n",
       "        [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "        [0., 0., 0., 1., 0., 0., 1., 0., 0., 0.],\n",
       "        [0., 0., 0., 0., 1., 0., 0., 0., 1., 0.],\n",
       "        [0., 0., 0., 0., 0., 1., 1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get indices\n",
    "# apply indices\n",
    "mask = torch.zeros_like(t)\n",
    "mask.scatter(1, ind, 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[7, 3],\n",
       "        [3, 7],\n",
       "        [4, 8],\n",
       "        [4, 1],\n",
       "        [5, 7],\n",
       "        [5, 3],\n",
       "        [4, 1],\n",
       "        [6, 3],\n",
       "        [8, 4],\n",
       "        [6, 5]])"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.randn(4,4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.7469,  0.8553, -1.4941],\n",
       "         [-0.8793,  0.3501, -0.7851],\n",
       "         [ 1.6777, -0.4477, -0.7974],\n",
       "         [ 0.5982,  0.1408, -0.1138]],\n",
       "\n",
       "        [[ 0.0258, -0.1167,  1.4570],\n",
       "         [ 0.7951, -0.0026,  0.1722],\n",
       "         [-1.0470, -2.0572,  0.5841],\n",
       "         [-0.2269,  0.0349,  0.3652]],\n",
       "\n",
       "        [[ 0.6581, -1.2306,  0.2728],\n",
       "         [-1.6131, -0.1629,  1.4430],\n",
       "         [-0.5600, -0.6853,  1.3488],\n",
       "         [-0.1763, -0.4766, -0.0918]],\n",
       "\n",
       "        [[-0.1463, -1.1577,  0.1479],\n",
       "         [-1.0374, -2.0516, -0.3279],\n",
       "         [-0.9665, -0.1928,  0.5775],\n",
       "         [-0.0735, -1.0916,  0.6450]]])"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.return_types.topk(\n",
       "values=tensor([[[ 0.8553,  0.7469],\n",
       "         [ 0.3501, -0.7851],\n",
       "         [ 1.6777, -0.4477],\n",
       "         [ 0.5982,  0.1408]],\n",
       "\n",
       "        [[ 1.4570,  0.0258],\n",
       "         [ 0.7951,  0.1722],\n",
       "         [ 0.5841, -1.0470],\n",
       "         [ 0.3652,  0.0349]],\n",
       "\n",
       "        [[ 0.6581,  0.2728],\n",
       "         [ 1.4430, -0.1629],\n",
       "         [ 1.3488, -0.5600],\n",
       "         [-0.0918, -0.1763]],"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.topk(2, dim=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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